DA0-002 Practice Test 11 — 15 Questions

Question 1

A business user asks a data analyst to include several charts in a weekly report. The user wants to see the trend of sales over the last 12 months at a glance. Which chart type should the analyst use?

Accepted Answer

Line chart. A line chart is the correct choice because it is specifically designed to display trends over continuous time intervals, such as sales over 12 months. The x-axis represents time (months), and the y-axis represents sales values, allowing the user to quickly see upward, downward, or cyclical patterns. This aligns with the requirement to visualize a trend at a glance, which is a core strength of line charts in data visualization.

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Stacked bar chart

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Treemap

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Pie chart

Question 2

A data analyst creates a bubble chart showing country GDP (x-axis), life expectancy (y-axis), and population (bubble size). However, large bubbles overlap and obscure many data points. Which corrective action should the analyst take?

Accepted Answer

Set bubble opacity to 70%. Setting bubble opacity to 70% allows overlapping bubbles to become semi-transparent, so data points underneath remain visible. This technique preserves the original data representation (GDP, life expectancy, and population) without altering the chart's scale or removing data. It is a standard visualization practice for handling overplotting in dense scatter plots and bubble charts.

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Increase the chart canvas size

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Reduce all bubble sizes uniformly

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Remove outlier countries with large populations

Question 3

An analyst is presenting findings to a non-technical audience. The data shows a 20% increase in customer churn after a price change. Which presentation approach is BEST?

Accepted Answer

Use a simple bar chart comparing churn before and after. A simple bar chart visually and intuitively communicates the 20% increase in churn to a non-technical audience without requiring statistical literacy. This approach aligns with best practices for presenting data insights to stakeholders who need clear, actionable takeaways rather than technical details.

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Explain the p-value

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Provide the raw data table

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Show a complex statistical model

Question 4

A data analyst needs to present findings about customer churn to business stakeholders. The analysis identified that churn is highest among customers who have called customer support more than three times in the last month. Which of the following is the best way to communicate this insight?

Accepted Answer

A bar chart comparing churn rates for different support call counts.. A bar chart directly compares churn rates across discrete categories of support call counts (e.g., 0, 1, 2, 3, 4+ calls), making it easy for stakeholders to see the spike at 'more than three calls'. This aligns with the insight that churn is highest among customers with >3 support calls, and a bar chart is the standard visualization for comparing a continuous metric (churn rate) across categorical bins.

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A scatter plot to show the relationship between support calls and churn.

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A pie chart showing the proportion of churned vs. retained customers.

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A table of raw churn data by customer ID.

Question 5

A data analyst is creating a report on customer satisfaction scores across different regions. The analyst wants to highlight regions that are significantly below average. Which of the following statistical methods is most appropriate for identifying these outliers?

Accepted Answer

Box plot with interquartile range (IQR) to identify outliers.. A box plot with interquartile range (IQR) is the most appropriate method because it explicitly identifies outliers as data points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. This directly addresses the analyst's goal of highlighting regions significantly below average, as the IQR method is a standard statistical technique for detecting extreme values in a distribution.

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Bar chart with average line.

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Pie chart of satisfaction categories.

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Scatter plot of satisfaction vs. region.

Question 6

A financial analyst at a bank is preparing a report on loan default risks to the risk management committee. The committee includes both technical (quantitative analysts) and non-technical (business managers) members. The analyst has built a logistic regression model that outputs probability scores for default. The model's performance is good, but the committee wants to understand the key drivers of default. The analyst needs to communicate both the model's accuracy and the impact of each feature. The report should be concise and persuasive, leading to policy changes. What is the best approach?

Accepted Answer

Use a waterfall chart showing the impact of each feature on a sample prediction.. A waterfall chart visually decomposes a single prediction into the additive contributions of each feature, making it intuitive for both technical and non-technical stakeholders to see which factors drive default risk. This approach directly addresses the committee's need to understand key drivers while keeping the report concise and persuasive for policy changes, unlike abstract metrics or tables.

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Provide a technical white paper.

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Present a feature importance bar chart and a table of coefficients.

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Show the confusion matrix and AUC-ROC curve.

Question 7

A data analyst is presenting results from a customer segmentation analysis to a non-technical audience. The segmentation was performed using k-means clustering, and the analyst wants to explain the characteristics of each cluster. Which visualization is most appropriate for this audience?

Accepted Answer

A bar chart comparing the average values of key metrics for each cluster.. A bar chart comparing average values of key metrics per cluster is the most appropriate choice because it directly translates the numerical output of k-means clustering into a simple, intuitive visual that non-technical audiences can easily interpret. Unlike complex multivariate plots, a bar chart focuses on the distinct characteristics of each cluster without requiring knowledge of clustering algorithms or dimensionality reduction. This aligns with the DA0-001 objective of communicating data insights effectively to stakeholders.

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A parallel coordinates plot displaying all variables used in clustering.

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A heatmap of the distance matrix between cluster centroids.

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A scatter plot with two principal components showing cluster boundaries.

Question 8

An analyst needs to combine two datasets from different sources that share a common key but have different levels of granularity. Dataset A has daily sales per store, Dataset B has hourly foot traffic per store. The analyst wants to analyze correlation. Which approach is appropriate?

Accepted Answer

Aggregate Dataset B to daily level before merging. Aggregating Dataset B (hourly foot traffic) to the daily level ensures both datasets share the same granularity before merging on the common key (store and date). This allows a valid correlation analysis between daily sales and daily foot traffic without introducing artificial patterns or data duplication. Merging at mismatched granularities would violate the assumption that each row represents a comparable unit of observation.

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Use an outer join and keep all rows

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Disaggregate Dataset A to hourly level by dividing daily sales by hours

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Join on store and date without aggregation

Question 9

Which THREE of the following are properties of ratio data? (Choose THREE.)

Accepted Answer

Supports multiplication and division. Ratio data supports multiplication and division because it has a true, meaningful zero point that indicates the absence of the measured attribute. This allows ratios to be computed (e.g., one value is twice another), which is a defining property of ratio scales in measurement theory.

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Data can be categorized into groups

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Allows negative values

Question 10

An analyst is creating a Tableau dashboard for a logistics company. They need to allow users to filter data by region and time period, and also want to show a tooltip with detailed shipment information. Which THREE Tableau features should be used? (Choose three.)

Accepted Answer

Tooltips. Parameters allow user input for filtering, dashboard actions enable interactions across sheets, and tooltips display details on hover. Calculated fields are for custom calculations, and marks are for visual encoding.

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Marks

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Calculated fields

Question 11

Refer to the exhibit. A data analyst is creating a report that includes customer transaction data from 6 years ago. According to the policy, what should the analyst do?

Accepted Answer

Exclude the data because it exceeds the retention period. The data retention policy specifies that customer transaction data must be retained for only 5 years. Since the data is from 6 years ago, it exceeds the retention period and must be excluded from the report to comply with data governance and regulatory requirements. Including or modifying such data would violate policy and potentially expose the organization to legal or compliance risks.

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Anonymize the data before inclusion

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Flag the data for review

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Include the data since it is valuable for analysis

Question 12

A company is designing a data pipeline to process streaming data from social media feeds. Which THREE of the following are characteristics of streaming data? (Select THREE).

Accepted Answer

Data is unbounded and infinite. Streaming data is inherently unbounded and infinite because social media feeds generate a continuous, never-ending flow of events. Unlike batch data, there is no natural end to the stream; new tweets, posts, or interactions arrive constantly, making the dataset theoretically infinite in size.

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Data is processed in micro-batches

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Data is stored permanently before processing

Question 13

Which of the following is a key performance indicator (KPI) tied to a strategic objective, as opposed to a metric?

Accepted Answer

Customer satisfaction score (CSAT) as a strategic goal. A key performance indicator (KPI) is a measurable value that demonstrates how effectively a company is achieving a key strategic objective. Here, 'Customer satisfaction score (CSAT) as a strategic goal' explicitly ties the metric to a strategic objective (e.g., improving customer loyalty), whereas a plain metric like 'average handle time' is an operational measure without a direct strategic link. In data visualization, KPIs are often displayed on dashboards with targets and thresholds to track progress toward high-level business goals.

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Average handle time

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Number of daily logins

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Server uptime percentage

Question 14

A data scientist applies K-means clustering to a customer dataset. The elbow method suggests using 4 clusters. After running K-means with k=4, the within-cluster sum of squares (WCSS) is plotted against k, and the elbow is at k=4. What does this indicate?

Accepted Answer

Increasing k beyond 4 would not significantly reduce WCSS.. The elbow method suggests that increasing k beyond 4 yields diminishing returns in reducing WCSS; k=4 is a good trade-off.

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The data naturally forms 4 clusters with no noise.

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The algorithm converged to a local minimum.

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The model has overfit the data.

Question 15

A data analyst uses linear regression to model the relationship between advertising spend and sales. The residual plot shows a clear U-shaped pattern. What assumption is violated?

Accepted Answer

Linearity. The U-shaped pattern in the residual plot indicates that the relationship between advertising spend and sales is not linear; the model fails to capture the curvature in the data. Linear regression assumes a straight-line relationship between predictors and the response, so a systematic pattern like a U-shape directly violates the linearity assumption. This means the model is misspecified and requires a transformation or a nonlinear modeling approach.

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Independence of residuals

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Homoscedasticity

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Normality of residuals